A new method of unsupervised collaborative calculation of the correspondence between the three-dimensional (3D) shape collections was proposed aiming at the problem that the calculation accuracy of the correspondence between the 3D shape collections of the near-isometric non-rigid was not high. The feature extraction module of the 3D point cloud was used to obtain the features after fusing low-dimensional features with richer location and detail information and high-dimensional features with richer semantic information. The extracted fusion features were converted into spectral descriptors in the unsupervised deep functional maps module, and the matrix of the functional map was calculated. The optimal function mapping matrix was obtained by applying weighted regularization constraints to the matrix. The optimal objective function was solved by combining the cycle-consistency constraint and functional maps theory in the shape collection correspondence cooperative calculation module in order to obtain the optimal shape collection correspondence. The experimental results showed that the geodesic errors of the 3D shape collection correspondence constructed by this algorithm on the FAUST, SCAPE and TOSCA datasets were smaller than the current commonly used methods. The mapping results are more smoother and the correspondence is more accurate, which has good generalization ability.